The book proposes a systematic approach to big data collection documentation and development
of analytic procedures that foster collaboration on a large scale. This approach designated as
data factoring emphasizes the need to think of each individual dataset developed by an
individual project as part of a broader data ecosystem easily accessible and exploitable by
parties not directly involved with data collection and documentation. Furthermore data
factoring uses and encourages pre-analytic operations that add value to big data sets
especially recombining and repurposing.The book proposes a research-development agenda that can
undergird an ideal data factory approach. Several programmatic chapters discuss specialized
issues involved in data factoring (documentation meta-data specification building flexible
yet comprehensive data ontologies usability issues involved in collaborative tools etc.). The
book also presents case studies for data factoring and processingthat can lead to building
better scientific collaboration and data sharing strategies and tools.Finally the book
presents the teaching utility of data factoring and the ethical and privacy concerns related to
it.Chapter 9 of this book is available open access under a CC BY 4.0 license at
link.springer.com
